Dense disparity maps used for stereo matching are used in many applications, including image-based rendering, 3-D scene reconstruction, robot vision, and tracking. Such applications of the stereo matching often presume a knowledge of an appropriate disparity search range, or use a fixed range.
In practice, the disparity search range of a scene facilitates the use of number of the stereo matching methods. The lack of the disparity search range results in a need to search over a wider range of candidate disparity values, which generally requires more computation and memory. More importantly, most stereo matching methods are likely to get trapped in local minima when given an inappropriate search range, which can compromise a quality of the disparity map.
However, the determination of the disparity search ranges is a complex problem, especially when, for example, the scene or camera configuration changes over time. One conventional method for determining a maximum disparity range is based on statistical analysis of the spatial correlation between stereo images. However, that method assumes that there are only positive disparities between stereo images.
Another disparity search range estimation method is based on confidently stable matching. In that method, the disparity search range is determined by setting an initial search range to a size of the image, and then performing the matching in a hierarchical manner.
Other methods are based on depth estimation techniques that directly impose temporal constraints as part of the estimation process. However, such techniques are prone to false matches, and incorrect estimation results without appropriate search ranges.
Accordingly, it is desired to provide a method for determining the disparity search range in the stereo video.